Global sovereign debt & fiscal health signals (debt trajectories, anomalies, drift forecasts)

Name: Global sovereign debt & fiscal health signals (debt trajectories, anomalies, drift forecasts)
Creator: Sovereign Debt Signals (derived)
License: https://www.imf.org/external/datamapper/
Keywords: sovereign-debt, fiscal, debt-sustainability, current-account, twin-deficit, volatility, momentum, anomaly-detection, forecasting, signals, imf, weo

Annual ML-enriched sovereign debt signals for ~190 economies, derived from IMF World Economic Outlook data: government gross debt (% of GDP), 1-year and 5-year debt changes (percentage points), the current account balance (% of GDP) for twin-deficit analysis, 30-period change volatility, 3-year momentum, 3-sigma anomaly flags versus a trailing 10-year baseline, naive-drift 1-year forecasts, and a per-year cross-country volatility rank. Raw indicators: International Monetary Fund (World Economic Outlook); signals computed locally. All rows carry ISO alpha-3 country_code and join cleanly with other macro datasets.

Source: Sovereign Debt Signals (derived)29,037 lignesMis à jour: 22/09/2026

sovereign-debtfiscaldebt-sustainabilitycurrent-accounttwin-deficitvolatilitymomentumanomaly-detectionforecastingsignalsimfweo

Qualité

92.5

Attribution

International Monetary Fund (World Economic Outlook; derived signals by Frontier Data Hub)

Schéma

Colonne	Type	Description
date	string	Observation year (January 1 of the WEO reference year).
country	string
country_code	string	ISO 3166-1 alpha-3 country code (IMF WEO economy code mapped to ISO).
series_id	string	Indicator code: DEBT_LEVEL (general government gross debt, % of GDP), DEBT_CHANGE_1Y (1-year pp change), DEBT_MOMENTUM_5Y (5-year pp change), CA_BALANCE (current account balance, % of GDP).
series_label	string	Human-readable IMF WEO indicator name for the series.
value	float	WEO indicator value in the indicator's native units (percent of GDP for DEBT_LEVEL and CA_BALANCE; percentage points of GDP for the change/momentum series).
volatility_30d	float
momentum_3m	float
anomaly_flag	integer
forecast_1m	float
rank	integer

Exemple de lignes

date	country	country_code	series_id	series_label	value	volatility_30d	momentum_3m	forecast_1m	rank
1980-01-01	Angola	AGO	CA_BALANCE	Current account balance, % of GDP	0.8	—	—	—	—
1980-01-01	Albania	ALB	CA_BALANCE	Current account balance, % of GDP	0.1	—	—	—	—
1980-01-01	United Arab Emirates	ARE	CA_BALANCE	Current account balance, % of GDP	24.2	—	—	—	—
1980-01-01	Argentina	ARG	CA_BALANCE	Current account balance, % of GDP	-1.1	—	-1.9000000000000001	—	—
1980-01-01	Australia	AUS	CA_BALANCE	Current account balance, % of GDP	-2.4	—	-2.5	—	—

Télécharger un échantillon

Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).

Télécharger l'exemple CSV Télécharger l'exemple JSON

Utiliser avec un LLM

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/debt_signals/global_sovereign_debt_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/debt_signals/global_sovereign_debt_signals").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

Point d’accès API: https://datazimuts.com/v1/datasets/debt_signals/global_sovereign_debt_signals

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.